A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide-GPCR interaction prediction

G protein-coupled receptors (GPCRs) are among the most important drug targets, and peptide therapeutics are rapidly emerging. However, accurate prediction of peptide-GPCR interactions (PepGI) remains challenging due to the scarcity of high-quality data and the poor generalization of existing drug-target interaction (DTI) models, which are largely trained on small molecule data. Here, we introduce a progressive fine-tuning framework with a dynamic parameter selection strategy that adaptively selects critical fine-tuning parameters using Fisher information. Our method begins with pretraining on a large small molecule-GPCR dataset, followed by intermediate fine-tuning on peptide-target data to alleviate the representation mismatch across heterogeneous ligand modalities. Finally, the task-specific fine-tuning is performed on the low-resource PepGI scenario. Extensive experiments show that our approach significantly outperforms baselines across multiple evaluation metrics, and exhibits robust generalization under few-shot and practical cold-start settings. Overall, this work offers an effective solution for low-resource peptide-GPCR prediction and presents a transferable framework for cross-structure DTI modeling.